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Author Zeppelzauer, Matthias ♦ Poier, Georg ♦ Seidl, Markus ♦ Reinbacher, Christian ♦ Schulter, Samuel ♦ Breiteneder, Christian ♦ Bischof, Horst
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2016
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword 3D rock-art analysis ♦ 3D segmentation ♦ Semi-automatic segmentation ♦ Surface texture analysis
Abstract Petroglyphs (rock engravings) have been pecked and engraved by humans into natural rock surfaces thousands of years ago and are among the oldest artifacts that document early human life and culture. Some of these rock engravings have survived until the present and serve today as a unique document of ancient human life. Since petroglyphs are pecked into the surface of natural rocks, they are threatened by environmental factors such as weather and erosion. To document and preserve these valuable artifacts of human history, the 3D digitization of rock surfaces has become a suitable approach due to the development of powerful 3D reconstruction techniques in recent years. The results of 3D reconstruction are huge 3D point clouds which represent the local surface geometry in high resolution. In this article, we present an automatic 3D segmentation approach that is able to extract rock engravings from reconstructed 3D surfaces. To solve this computationally complex problem, we transfer the task of segmentation to the image-space in order to efficiently perform segmentation. Adaptive learning is applied to realize interactive segmentation and a gradient preserving energy minimization assures smooth boundaries for the segmented figures. Our experiments demonstrate the efficiency and the strong segmentation capabilities of the approach. The precise segmentation of petroglyphs from 3D surfaces provides the foundation for compiling large petroglyph databases which can then be indexed and searched automatically.
Description Author Affiliation: Graz University of Technology (Poier, Georg; Reinbacher, Christian; Schulter, Samuel; Bischof, Horst); Vienna University of Technology (Breiteneder, Christian); St. Pölten University of Applied Sciences (Zeppelzauer, Matthias; Seidl, Markus)
ISSN 15564673
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2016-09-01
Publisher Place New York
e-ISSN 15564711
Journal Journal on Computing and Cultural Heritage (JOCCH)
Volume Number 9
Issue Number 4
Page Count 30
Starting Page 1
Ending Page 30


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Source: ACM Digital Library